New Orleans and the Changing Climate
Khalil Johnson
2/19/2016
New Orleans Heat
## function (...)
## {
## dots = list(...)
## if (length(dots) == 0)
## return()
## if (is.null(names(dots)) && length(dots) == 1 && is.list(dots[[1]]))
## if (length(dots <- dots[[1]]) == 0)
## return()
## defaults <<- merge(dots)
## invisible(NULL)
## }
## <environment: 0x22b18f0>Where Do We See the Most Temperature Change in New Orleans?
plot(TMAX~DATE, import)
strDates <- as.character(import$DATE)
head(strDates)## [1] "18930101" "18930102" "18930103" "18930104" "18930105" "18930106"import$NewDate <- as.Date(strDates, "%Y%m%d")
## [1] NEW ORLEANS AUDUBON LA US
## Levels: NEW ORLEANS AUDUBON LA US
##
## Call:
## lm(formula = TMAX ~ NewDate, data = LosAngeles)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.033 -4.370 1.157 5.692 13.736
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.616e+01 3.617e-02 723.208 < 2e-16 ***
## NewDate 7.539e-06 2.548e-06 2.959 0.00308 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.932 on 43027 degrees of freedom
## (1154 observations deleted due to missingness)
## Multiple R-squared: 0.0002035, Adjusted R-squared: 0.0001803
## F-statistic: 8.758 on 1 and 43027 DF, p-value: 0.003084
In August of 2005 most of the nation watched as Hurricane Katrina devastated New Orleans. Suddenly, most of the nation came to the sobering realization that not only was climate change real, but a phenomenon that we would see much more of as time passed. The city of New Orleans has recovered since then, and is consistently on guard against inevitable threatening weather events as a result of climate change. In fact, over recent months The Picayune Times has repeatedly featured articles cautioning the public to prepare for unprecedented heat waves. The city is known for its particularly warm and humid weather, but as of late the birthplace of jazz has been fried like frog legs by record-breaking temperatures. Weather stations in and around the city have collected relevant data since 1900, and suggest an upward trend in temperatures, especially in the summer months. The hottest months have extended their stays, and that influence has caused fall months to warm. These spikes have made the city’s underserved communities more vulnerable, and as a region that is particularly sensitive to climate change, attention must be paid to these changes.
Selecting for 1 Month – June
Perhaps, we can can understand the temperature change in New Orleans if we analyze for just one month at a time!
This is June
#plot(MonthlyMean$TMAX[MonthlyMean$Month=="06"], ty='l')
plot(TMAX~YEAR, data=MonthlyMean[MonthlyMean$Month=="06",], ty='l',ylab = "Temperature (degrees Farenheit")
May.lm <- lm(TMAX~YEAR, data=MonthlyMean[MonthlyMean$Month=="06",])
summary(May.lm)##
## Call:
## lm(formula = TMAX ~ YEAR, data = MonthlyMean[MonthlyMean$Month ==
## "06", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6171 -0.6783 0.0037 0.4919 2.8978
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 25.278416 5.291573 4.777 5.12e-06 ***
## YEAR 0.003702 0.002706 1.368 0.174
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.075 on 119 degrees of freedom
## Multiple R-squared: 0.01548, Adjusted R-squared: 0.007211
## F-statistic: 1.872 on 1 and 119 DF, p-value: 0.1739abline(coef(May.lm), col="red")
The Times-Picayune reported in 2015 that June brought a heatwave of 21 days (Schleifstein,2015). New Orleans also had the hottest day on record in 2009 on June 24th of 104 degrees (Kamenitz, 2009). Without a doubt patterns of that nature have contributed to the overall rise in temperatures in June. Overtime, one can see a sharp incline in the summer month. Pvalue, here it is highlighy significant because it is less that p<.05
plot(MonthlyMean$TMAX[MonthlyMean$Month=="07"], ty='l')
plot(TMAX~YEAR, data=MonthlyMean[MonthlyMean$Month=="07",], ty='l')
May.lm <- lm(TMAX~YEAR, data=MonthlyMean[MonthlyMean$Month=="07",])
summary(May.lm)##
## Call:
## lm(formula = TMAX ~ YEAR, data = MonthlyMean[MonthlyMean$Month ==
## "07", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.71738 -0.60994 0.09456 0.66450 1.75630
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.290226 4.710914 3.246 0.001525 **
## YEAR 0.009120 0.002409 3.786 0.000243 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9566 on 118 degrees of freedom
## Multiple R-squared: 0.1083, Adjusted R-squared: 0.1007
## F-statistic: 14.33 on 1 and 118 DF, p-value: 0.0002427abline(coef(May.lm), col="red")
The same New Orleans based newspaper reported that July reported 31 days, an entire month, of abnormally high temperatures. July, while a summer month, historically has not reached these hights in temperature. There is a clear trend here that indicates a more dangerous summer in the city.
plot(MonthlyMean$TMAX[MonthlyMean$Month=="08"], ty='l')
plot(TMAX~YEAR, data=MonthlyMean[MonthlyMean$Month=="08",], ty='l')
May.lm <- lm(TMAX~YEAR, data=MonthlyMean[MonthlyMean$Month=="08",])
summary(May.lm)##
## Call:
## lm(formula = TMAX ~ YEAR, data = MonthlyMean[MonthlyMean$Month ==
## "08", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7088 -0.7040 0.0077 0.7291 2.7293
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.493503 5.751326 2.694 0.00808 **
## YEAR 0.008994 0.002941 3.058 0.00275 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.166 on 119 degrees of freedom
## Multiple R-squared: 0.07285, Adjusted R-squared: 0.06506
## F-statistic: 9.351 on 1 and 119 DF, p-value: 0.002754abline(coef(May.lm), col="red")
August has a less dramatic incline, but still is significant given the temperatures in July. This month saw a 25 day heatwave as well in (year).
plot(MonthlyMean$TMAX[MonthlyMean$Month=="09"], ty='l')
plot(TMAX~YEAR, data=MonthlyMean[MonthlyMean$Month=="09",], ty='l')
May.lm <- lm(TMAX~YEAR, data=MonthlyMean[MonthlyMean$Month=="09",])
summary(May.lm)##
## Call:
## lm(formula = TMAX ~ YEAR, data = MonthlyMean[MonthlyMean$Month ==
## "09", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.7705 -0.9406 0.0559 0.8331 2.8483
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 26.152654 6.311315 4.144 6.42e-05 ***
## YEAR 0.002588 0.003229 0.802 0.424
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.273 on 119 degrees of freedom
## Multiple R-squared: 0.00537, Adjusted R-squared: -0.002988
## F-statistic: 0.6425 on 1 and 119 DF, p-value: 0.4244abline(coef(May.lm), col="red")
This month is what should alarm residents of New Orleans. The summer concludes in August and the high temperatures should subside in September. This month cools the city down and prepares New Orleanians for fall temperatures. The trend shows rising temperatures in this month, thus extending the the summer to unfamiliar territory.
plot(MonthlyMean$TMAX[MonthlyMean$Month=="10"], ty='l')
plot(TMAX~YEAR, data=MonthlyMean[MonthlyMean$Month=="10",], ty='l')
May.lm <- lm(TMAX~YEAR, data=MonthlyMean[MonthlyMean$Month=="10",])
summary(May.lm)##
## Call:
## lm(formula = TMAX ~ YEAR, data = MonthlyMean[MonthlyMean$Month ==
## "10", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.1768 -0.7259 -0.0672 0.9157 4.7269
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 14.823604 8.079925 1.835 0.069 .
## YEAR 0.006271 0.004133 1.518 0.132
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.642 on 120 degrees of freedom
## Multiple R-squared: 0.01883, Adjusted R-squared: 0.01065
## F-statistic: 2.303 on 1 and 120 DF, p-value: 0.1318abline(coef(May.lm), col="red")
October experiences the same upward trend. Halloween, a wildly popular holiday in New Orleans, was the hottest on record. Summer has officially extended deep into the fall, close to the winter season.
This will have an enourmous impact on vulnerable communities. Eliot Kamenitz of The Times-Picayune details the drastic efforts that the city must take to protect underserved citizens from the heat.
Heatwaves are the most dangerous weather events, and the number of deaths they claim are steadily on the rise. The dangerous mixture of both heat and moisture make for incredibly dangerous temperatures and humidity levels that all but ensure an emergency room full of heat stricken patients. Patrick Kinney, director of Climate and Health Program at Columbia Univresity, reported that heat-related deaths in the city of Manhatten for example may rise about 20% over the next decade as a result of climate change (Peeples,2013). This weather is descrimintatory in its impacts as well, especially in heat prone environments. African Americans are 50 times more likely than their white counterparts to live in such environments, therefore, having higher cases of heat related illnesses (Peeples, 2013). Socioeconomically depressed communities are also at a higher susceptibility to heatwaves because of their fear of high energy bills.
While the share of New Orleans’ poor residents living in neighborhoods dropped from 38% in 2000 to 30% in 2013 (Holmes,2015). There remains a heavy concentration of low income groups in places like the Orleans Parish. This socioeconomic disadvantage opens the doors for others as well, like health issues for example. Heatwaves excacerbate problems like these, and even create new ones: rashes, strokes, etc. The city of New Orleans must pay close attention to these temperature changes as climate changes progresses.
##
## Call:
## lm(formula = TMAX ~ YEAR, data = MonthlyMean[MonthlyMean$Month ==
## "12", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7728 -1.3943 0.0562 1.1955 5.1660
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.088843 10.013752 1.007 0.316
## YEAR 0.004302 0.005124 0.840 0.403
##
## Residual standard error: 1.988 on 117 degrees of freedom
## Multiple R-squared: 0.00599, Adjusted R-squared: -0.002506
## F-statistic: 0.7051 on 1 and 117 DF, p-value: 0.4028
TMIN
We create a monthly mean for each month.
## Month Year TMIN YEAR
## 1 01 1893 4.216129 1893
## 2 02 1893 12.389286 1893
## 3 03 1893 9.983871 1893
## 4 04 1893 16.289655 1893
## 5 05 1893 19.332258 1893
## 6 06 1893 22.280000 1893
##
## Call:
## lm(formula = TMIN ~ YEAR, data = MonthlyMeanTMIN[MonthlyMeanTMIN$Month ==
## "12", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.4490 -1.2880 -0.1926 1.0409 5.7261
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -35.039898 10.258092 -3.416 0.000875 ***
## YEAR 0.022114 0.005249 4.213 4.97e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.036 on 117 degrees of freedom
## Multiple R-squared: 0.1317, Adjusted R-squared: 0.1243
## F-statistic: 17.75 on 1 and 117 DF, p-value: 4.972e-05
##
## Call:
## lm(formula = PRCP ~ YEAR, data = YearlySum)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1138.53 -242.13 -15.49 244.43 1280.79
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 310.3891 1712.3277 0.181 0.856
## YEAR 0.6273 0.8758 0.716 0.475
##
## Residual standard error: 348.2 on 121 degrees of freedom
## Multiple R-squared: 0.004221, Adjusted R-squared: -0.004008
## F-statistic: 0.5129 on 1 and 121 DF, p-value: 0.4752## Time-Series [1:123, 1] from 1 to 123: 1426 1494 1858 1444 1349 ...
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr "PRCP"